 from our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE Conversation. Everyone, welcome to this CUBE Conversation here in Palo Alto, California, CUBE Studios. I'm John Furrier, the host of theCUBE. We're here with CUBE alumni and special guest, Mitt Wally, a president of products and marketing at Informatica. I'm Mitt, great to see you. It's been a while, it's been a couple months. How's things? Good to be back, as always. Welcome back. Okay, so Informatica World's coming up. We have a whole segment on that, but we've been covering you guys for a long, long time. Data is at the center of the value proposition again and again, it's more amplified now. The fog is lifting in the world is now seeing what, I think we were talking about four years ago, with data. What's new? What's the big trends going on that you guys are doubling down on? What's new? What's changed? Give us the update. I think we've been talking for the last couple of years. I think you're right. Data is becoming more and more important. I think three things we see a lot. One is obviously you saw this whole world digital transformation. I think that definitely has picked up so much steam now. I mean, every company is going digital and obviously that creates a whole new paradigm shift for companies to almost recreate themselves, reinvent themselves and data becomes the new definition. And that's what we call, I think, as you saw it Informatica World even before, the data 3.0 world, where data is the center of everything, right? And basically, you see the volume of data growth, the utilization of data to make decisions, whether it's decisions of the shop floor, decisions basically related to cyber security or whatever it is. And the key that we are seeing very different now is the whole AI assisted data management. I mean, the scale of complexity, the scale of growth, you know, multi-cloud, multi-platform, all the stuff that's in front of us, it's very difficult to run the old way of doing things. So that's why we see, the one thing that we see a whole lot is AI is becoming a lot more mainstream. Still early days, but it's assisting the whole ability for companies to what I call exploit data to really become a lot more transformative. You've been on this for a while. Again, when we had, go back to theCUBE archives, we almost pulled out clips from two years ago, be relevant today. You know, the data control, understanding, you know, the understanding where the data governance is, that's always a foundational thing. But you guys nailed the chat box, you've been doing AI, those previous announcements. This is putting a lot of pressure on you, the president of the products. You got to get this out there. What's new? What's happening inside Informatica? He's peddling as fast as you can. What are some of the updates? Give us the... For example, always just like the duck, right? You know, you're really swimming, you feel like you're calm at the top and then you're really peddling. Now I think it's great for us. I think I look at AI, you see AI is like, there's so much fun around it, machine learning AI. We look at it, it's two different ways. One is how we leverage machine learning within our products to help our customers. Making it easy for them to, as I said, so many different data types. Think of IoT data, unstructured data, streaming data. How do you bring all that stuff together and marry it with your existing transactional data to make sense? So we are leveraging a lot of machine learning to make the internal products a lot more easier to consume, a lot more smarter, a lot more richer. The second thing is that we, what we call is our AI, Clare, which we unveiled if you remember a couple of years ago at Informatica World, how that then helps our customers make smarter decisions in the world of data science and all these new data workbenches. You know, the old statistical models are only as good as they can ever be. So we are leveraging helping our customers to think the value proposition of our AI, Clare. Then to what I make things that, you know, find patterns that, you know, statistical models cannot. So to me, I look at both of those really, leveraging ML to shape our products, which is where we do a lot of innovation. And then creating our AI, Clare, to then help customers make smarter decisions, easier decisions, complex decisions, which what I call the humans or the statistical models really cannot. Well, this is the balance between machines and humans working together. And you guys have nailed this before. And I think this was two years ago, I started to hear the words, you know, land, adopt, expand from you guys, right? Which is, you got to get adoption. And so as you're iterating on this product focus, you got to get it working to make sure your products. Big, big maniacal focus of that one. So talk about what you've learned there because that's a hard thing. You guys are doing well at it. You got to get adoption means you got to listen to customers. You got to do the course correction. What's the learnings coming out of that piece of that? That's actually such a good point. We made such, see we were always a very customer-centric company, but as you said, like as the world shifted towards a new subscription, cloud model, we really focused on helping our customers adopt our products. And you know, this new world customers are also struggling with new architectures and everything. So we double down on what we call customer success, making sure we can help our customers adopt the products. And by the way, it's dual benefit. Our customers get value very quickly. And of course, we believe in what we call a customer for life. Our ability to then grow with our customers and help them deliver value becomes a lot better. So we really, so we have globally across the board, customer success managers, we really invest in our customers. The moment we, a customer buys a product from us, we directly engage with them to help them understand, for this use case, how will you implement the product? It's not just self-serious. One thing I appreciate, because you know, how hard is it to build products these days, especially with the velocity of change. But it's also, when you have in the large scale data, you need automation. You got to have machine learning. You got to have these disciplines. Sure. And this is both on your end, but also for the customer. Any updates on the Claire and some customer learnings that you're seeing that are turning into either use cases or best practices? So many of them. So take a simple example, right? I mean, we think of, we take these things for granted, right? I mean, take, you know, we talk about IoT these days, right? I mean, all of these sensors, we were streaming data, right? Or even robots in the shop floor. So much of that data has no schema, no structure, no definition, it's coming, right? Netflix data has to, and for customers, there's a lot of volume on it. A lot of it could be junk, right? So how do you first take that volume of data, create some structure to it for you to do analytics? You can only do analytics if you put some structure to it, right? So first thing is that we leverage Claire. We help our customers create what I call schema and you can create some structure to it. Then what we do allow is basically Claire through Claire. It can naturally bring what we have the data quality on top of it. Like how much of it is irrelevant? How much of it is noise? How much of it really makes sense? So then, as you said it, signal from the noise. We're helping our customers get signal from the noise of data. That's where AI comes very handy because it's a very manual, cumbersome, time consuming and sometimes very difficult to do. So that's an area where we have leveraged creating structure, adding data quality on top and finding rules that didn't probably naturally didn't exist that you and me would be able to see. Machines are able to do it. And to your point, our belief is, this is my 100% belief, we believe in AI assisting the humans. We have given the value of Claire to our users that it complements you. And that's where we are trying to help our users get more productive and deliver more value faster. Productivity is multi-fold. It's like also efficiency. You don't want people wasting time on a project that can be automated. So you can focus that valuable resource somewhere else. Okay, so let's shift gears onto Informatica world coming up. Let's spend some time on that. What's the focus this year, the show? It's coming up right around the corner. What's going to be the focus? What's going to be the agenda? What's on the plate? Give you a quick sense of how it'll shape up. It's probably going to be our biggest Informatica world. So it's 20th year. Again, back in Vegas. You know, we love Vegas, of course. We have obviously a couple of days lined up over there. I know you guys will be there too. Great set of speakers. Obviously, we'll have main stage speakers like, so we'll have some, the CEO of Google Cloud, Thomas Corrine is going to be there. We'll have on main stage with Anil. We'll have the CEO of Databricks, Ali, with me. We'll also have the CMO of AWS Aerial there. Then we have a couple of customers lined up. Simon from Credit Suisse, Danielle's CEO from Nissan. We also have the head of AI, Simon Gogenheimer from Microsoft, as well as the chief product officer of Tableau, Francois on main stage. So we have a great lineup of speakers, customers, and some of our very, very strategic partners with us. If you remember last year, we also had Scott Guthrie there on main stage. 80 plus sessions, pretty much 90% are led by customers. We have 70 to 80 customers presenting with us. Technical sessions are going to be a secret. Technical, business. We have all kinds of tracks. We have hands on labs. We have learnings. Customers really want to come learn our products, talk to the experts. Some want to talk to the product managers. Some want to talk to the engineers. Literally so many hands on labs. It's going to be a full blown couple of days for us. What's the pitch for someone watching that has never been in an informatic world? Why should they come to the show? I always tell them three things. Number one is that, A, it's a user conference for our customers to learn all things about data management. And then of course in that context, they learn a lot about, so they learn a lot about the industry. So day one, we kick it off by market perspectives. We're giving a sense of where the market is going. How everybody should be stepping back from the day to day and understanding, where are these digital transformation? AI, where is the world of data going? We have some great analysts coming talking, some customers talking. We'll be talking about futures over there. Then it is all about hands on learning, right? Learning about the products. Hearing from some of these experts, right? From the industry experts, as well as our customers teaching what to do, what not to do and networking. It's always great to network, right? It's a great place for people to learn from each other. So it's a great forum for those three things. But the theme this year is all around AI. I talked about Claire. In fact, our tagline this year is clarity unleashed. We really want to basically, AI has been developing for the last couple of years. It is becoming a lot more mainstream for us in our offerings. And this year, we really are taking it mainstream. So it's kind of like unleashing it where everybody can genuinely use it, truly use it for their day to day data management activities. Clarity is a great theme. I mean, plays on Claire, but this is what we're starting to see some visibility into some clear, economic benefits, business benefits, technical benefits, kind of all starting to come in. How would you categorize those three areas? Because that's generally the consensus these days is that what was once a couple of years ago was like foggy, what do you see? Now you're starting to see that lift. You're seeing economic, business, and technical benefits. So to me, it's all about economic and business. Technology plays a role in driving value for the business, right? I'm a firm believer in that, right? And if you think about some of the trends today, right? A billion users are coming into play that'll be assisted by AI. Data is doubling every year. You know the volume of data and the amount of, and I always say business users today, I mean, when I run a business, I want, I always say tomorrow's data, yesterday to make a decision today. It's just in time. And that's where AI comes into play. So our goal is to help organizations transform themselves. Truly, you know, be more productive, reduce operational costs. By the way, governance and compliance, that's becoming such a mainstream topic. It's not just basically making analytical decisions. How do you make sure that your data is safe and secure? You don't want to get basically hit by any of these cyber attacks. They're all coming after data. So governance and compliance of data, that's becoming, so tools? You guys are right on the data thing. I want to get your reaction because you mentioned some stats. I have some stats here. Data explosion, 15.3 zettabytes per year in global traffic. 500 million business data users and growing 20 billion connected devices. One billion workers will be assisted by machine learning. So thanks for plugging those stats, but I want to get your reaction to some of these other points here. 80% of enterprises are looking at multi-cloud. They're really evaluating where the data sits in that kind of equation. And then the other thing is that the responsibility and role of the chief data officer. These are new dynamics. I think you guys will be addressing that in the event. Because organizational dynamics, skill gaps are issues, but also you have multi-cloud. You're talking about multi-cloud. I mean, that's a big thing. I mean, look, in the old world, John, heterogeneity has always existed in large enterprises, right? And it's going to stay here. In fact, I think it's not just cloud. Think of it this way. On-prem is still here. It's not going away. It's reducing in scope. But then you have this multi-cloud world, SaaS apps, pass apps, infrastructure. If I'm a customer, I want to do all of it. But the biggest problem comes as you said, is that my data is everywhere. How do I make sense of it? And then how do I govern it? Like my customer data is sitting somewhat in this SaaS app, in that platform, in this on-prem application transaction app running. How do I connect the three and how do I make sense it doesn't get? I can have a governance and control around it. That's where data management becomes more important, but more complex. But that's where AI comes into making it easier. One of the things we've seen a lot as you touched upon is the rise of the CDO. In fact, we have Danielle from Nissan. She's a CDO of Nissan North America on main stage, talking about her role and how they've leveraged data to transform themselves. That is something we're seeing a lot more because the role of the CDO in making sure there is not only a sense of governance and compliance, a sense of how to even understand the value of data across an enterprise. Again, see, one of the things we're going to talk about is its whole system thinking around data. We call it system thinking 3.0. Data is becoming a platform. See, there was OS at the hardware layer, whether it is server or compute, we believe that data is becoming a platform in itself. Whether you think about it in terms of scale, in terms of governance, in terms of AI, in terms of privacy, you have to think of data as a platform. That's the other big thing. I think that is a very powerful statement and I'd like to get your thoughts we've had many countries on-camera, off-camera, around product, Silicon Valley, venture capital, how startups can create value. One of the old adages used to be build a platform, that's your competitive strategy. There were a platform company and that was a strategic competitive advantage that was unique to the company and they created enablement. Facebook's a great example. They monetize all the data from the users. Look where they are. If you think about platforms today, it seems to be table stakes, not as a competitive advantage, a more of a foundational element of all businesses. Not just startups, enterprises. This seems to be a common thread. Do you agree with that, that platforms are becoming table stakes? Because if we have to think like systems people, whether it's an enterprise or a supplier, then holistically the platform becomes table stakes that could be on-prem or cloud. Your reaction to that. You agree? I'll say it slightly differently. Yes, I think platform is a critical component for any enterprise when they think of their end-to-end technology strategy because you can't do piece-meals. Otherwise you become a system integrator of your own, right? But it's not easy to be a platform player itself, right? Because as a platform player, the responsibility of what you have to offer your customer becomes a lot bigger. So we obviously have this intelligent data platform. But the other thing is that the role of the platform is different too. It has to be very modular and API driven. Nobody wants to buy a monolithic platform. I don't want to, as an enterprise, I don't want to implement five years of platform. You want, it's going to be like a Lego block, okay? It builds by itself, not monolithic, very API driven, micro services-based. And that's our belief that in the new world, yes, platform is very critical for you to accelerate your digital transformation journeys or data-driven digital transformation journeys. But the platform better be API driven, micro services-based, very nimble, that it's not a precursor to value creation but creates value as you go along. It all kind of depends on the customer. Get up a thin foundational data platform from you guys, for instance. And then what you're saying is compose of different components. So if, for example, you have a data integration platform, then you can do data quality on top. You can do master data management on top. You can provide governance. You can provide privacy. You can do cataloging. It all builds. It's not like, oh my gosh, I have to go do all these things over a course of five years. Then I'll get value. You've got to create value all along. Today's customers want value like in two months, three months. You don't want to wait for a year or two years. This is exactly what I think the kind of the operating system, systems mindset that you were referring to. This is kind of how enterprises are behaving now. This is the way that you see on-premise thinking around data and cloud, multi-cloud emerging. It's a systems view of distributed computing with the right block, Lego blocks. That's what our belief is. That's what we hear from customers. See, I spend most of my time traveling talking to customers. And we try to understand what customers want today. And you know, some of these latent demands that they have that they can't sometimes articulate. My job, I always end up on the road most of the time just hearing customers. And that's what they want. They want exactly to your point, a platform that builds, not monolithic, but they do want a platform. They do want to make it easy for them not to do everything piecemeal. Every project is a data project. Whether it's a customer experience project, whether it's a governance project, whether it is nothing but an analytical project. It's a data project. But you don't want to repeat it every time. That's what they want to do. I know you got a hard stuff but I want to give you thoughts on this because I've heard the word workload mentioned so many more times these in the past year. If there was a tag cloud of all the cube conversations where the word workload was mentioned would be the biggest font. Workload's been around for a while but now you're seeing more and more workloads coming on. That's more important for data. That workload's being tied into the data. And then sharing data across multiple workloads. That's a big focus. You see that same thing? We absolutely see that. And the unique thing that we see also is that new workloads are getting created and the old workloads are not going away. Which is where the hybrid becomes very important. See, we serve large enterprises and their goal is to have a hybrid. So I'm running a old transaction workload over here. I want to have an experimented workload. I want to start a new workload. I want all of them to talk to each other. I don't want them to become silos. And that's when they look to us to say connect the dots for me. You can be in the cloud. As an example, our cloud platform. You know, last time at Informatica, we talked about like it was at five trillion transactions a month. Today it's double that. Eight to 10 trillion transactions a month. Growing like crazy. But our traditional workload is also still there so we connect the dots for our customers. Amit, thank you for coming on and sharing the insights. Obviously you guys are doing well. We got 350,000 developers, billions in revenue. Thanks for coming on. Appreciate the insight and looking forward to Informatica world. Thank you very much. Amit Wally here inside the Cube. I'm John Furrier with the Cube Conversation in Palo Alto. Thanks for watching.